Can large language models democratize access to dual-use biotechnology?
Not on the map. Why?
Abstract (arXiv)
Large language models (LLMs) such as those embedded in 'chatbots' are accelerating and democratizing research by providing comprehensible information and expertise from many different fields. However, these models may also confer easy access to dual-use technologies capable of inflicting great harm. To evaluate this risk, the 'Safeguarding the Future' course at MIT tasked non-scientist students with investigating whether LLM chatbots could be prompted to assist non-experts in causing a pandemic. In one hour, the chatbots suggested four potential pandemic pathogens, explained how they can be generated from synthetic DNA using reverse genetics, supplied the names of DNA synthesis companies unlikely to screen orders, identified detailed protocols and how to troubleshoot them, and recommended that anyone lacking the skills to perform reverse genetics engage a core facility or contract research organization. Collectively, these results suggest that LLMs will make pandemic-class agents widely accessible as soon as they are credibly identified, even to people with little or no laboratory training. Promising nonproliferation measures include pre-release evaluations of LLMs by third parties, curating training datasets to remove harmful concepts, and verifiably screening all DNA generated by synthesis providers or used by contract research organizations and robotic cloud laboratories to engineer organisms or viruses.
Key facts. Cited by 35 papers in the map (data through 14 Oct 2026). Builds on 5 of its 30 references that are in the map. First posted on arXiv 6 Jun 2023.
Builds on
5 of 30 references.
References · 5
- 2017Deep reinforcement learning from human preferencescited by 3,912
- 2022Constitutional AI: Harmlessness from AI Feedbackcited by 3,050
- 2022Galactica: A Large Language Model for Sciencecited by 744
- 2023Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attackscited by 278
- 2023Emergent autonomous scientific research capabilities of large language modelscited by 132
Built on by
Citing papers per year: 2023: 10; 2024: 10; 2025: 5; 2026: 10.
35 papers.
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- 2023An Overview of Catastrophic AI Riskscited by 222
- 2023AI Deception: A Survey of Examples, Risks, and Potential Solutionscited by 188
- 2024AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathwayscited by 127
- 2024Black-Box Access is Insufficient for Rigorous AI Auditscited by 126
- 2023Frontier AI Regulation: Managing Emerging Risks to Public Safetycited by 98
- 2024Visibility into AI Agentscited by 79
- 2024Large Language Model Safety: A Holistic Surveycited by 59
- 2023Managing extreme AI risks amid rapid progresscited by 48
- 2023Hoodwinked: Deception and Cooperation in a Text-Based Game for Language Modelscited by 47
- 2024A Safe Harbor for AI Evaluation and Red Teamingcited by 47
- 2023BadLlama: cheaply removing safety fine-tuning from Llama 2-Chat 13Bcited by 36
- 2024Adversaries Can Misuse Combinations of Safe Modelscited by 32
23 more
- 2023Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectivescited by 31
- 2023Will releasing the weights of future large language models grant widespread access to pandemic agents?cited by 26
- 2024Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Researchcited by 15
- 2023Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Modelscited by 7
- 2026Measuring Mid-2025 LLM-Assistance on Novice Performance in Biologycited by 6
- 2023Towards Responsible Governance of Biological Design Toolscited by 5
- 2024To Err is AI : A Case Study Informing LLM Flaw Reporting Practicescited by 5
- 2025Mapping AI Benchmark Data to Quantitative Risk Estimates Through Expert Elicitationcited by 4
- 2025Misalignment or misuse? The AGI alignment tradeoffcited by 4
- 2024Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systemscited by 2
- 2025Supervision policies can shape long-term risk management in general-purpose AI modelscited by 2
- 2025A Survey on Training-free Alignment of Large Language Modelscited by 2
- 2026A Blind Spot in Alignment: Quantifying Biosecurity Risks in Large Language Modelscited by 2
- 2024CodeUnlearn: Amortized Zero-Shot Machine Unlearning in Language Models Using Discrete Conceptcited by 1
- 2025Can Large Language Models Design Biological Weapons? Evaluating Moremi Biocited by 1
- 2026David vs. Goliath: Verifiable Agent-to-Agent Jailbreaking via Reinforcement Learningcited by 1
- 2026RefusalBench: Why Refusal Rate Misranks Frontier LLMs on Biological Research Promptscited by 1
- 2026Measuring Biological Capabilities and Risks of AI Agentscited by 1
- 2026An Early Warning of Emerging Biosecurity Risks in Frontier LLMscited by 1
- 2026Sockpuppetting: Jailbreaking LLMs by Combining Prefilling with Optimizationcited by 0
- 2026A Threshold Exceedance Framework for CBRN Uplift Evaluation in Frontier Language Modelscited by 0
- 2026FUSE: An Evaluating Framework for Dangerous Capabilities of LLMscited by 0
- 2026Artificial intelligence and biosecurity: capabilities, threat pathways, and defense-in-depth governancecited by 0
- 2026Artificial intelligence and biosecurity: capabilities, threat pathways, and defense-in-depth governance14 Sep 2026
- 2026FUSE: An Evaluating Framework for Dangerous Capabilities of LLMs2 Sep 2026
- 2026A Blind Spot in Alignment: Quantifying Biosecurity Risks in Large Language Models3 Aug 2026
- 2026An Early Warning of Emerging Biosecurity Risks in Frontier LLMs20 Jul 2026
- 2026A Threshold Exceedance Framework for CBRN Uplift Evaluation in Frontier Language Models13 Jul 2026
- 2026Measuring Biological Capabilities and Risks of AI Agents18 Jun 2026
- 2026RefusalBench: Why Refusal Rate Misranks Frontier LLMs on Biological Research Prompts20 May 2026
- 2026Measuring Mid-2025 LLM-Assistance on Novice Performance in Biology18 Feb 2026
- 2026David vs. Goliath: Verifiable Agent-to-Agent Jailbreaking via Reinforcement Learning2 Feb 2026
- 2026Sockpuppetting: Jailbreaking LLMs by Combining Prefilling with Optimization19 Jan 2026
- 2025A Survey on Training-free Alignment of Large Language Models12 Aug 2025
- 2025Misalignment or misuse? The AGI alignment tradeoff4 Jun 2025
23 more
- 2025Can Large Language Models Design Biological Weapons? Evaluating Moremi Bio22 May 2025
- 2025Mapping AI Benchmark Data to Quantitative Risk Estimates Through Expert Elicitation6 Mar 2025
- 2025Supervision policies can shape long-term risk management in general-purpose AI models10 Jan 2025
- 2024Large Language Model Safety: A Holistic Survey23 Dec 2024
- 2024Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research9 Dec 2024
- 2024Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems30 Oct 2024
- 2024To Err is AI : A Case Study Informing LLM Flaw Reporting Practices15 Oct 2024
- 2024CodeUnlearn: Amortized Zero-Shot Machine Unlearning in Language Models Using Discrete Concept8 Oct 2024
- 2024Adversaries Can Misuse Combinations of Safe Models20 Jun 2024
- 2024AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathways4 Jun 2024
- 2024A Safe Harbor for AI Evaluation and Red Teaming7 Mar 2024
- 2024Black-Box Access is Insufficient for Rigorous AI Audits25 Jan 2024
- 2024Visibility into AI Agents23 Jan 2024
- 2023Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Models22 Dec 2023
- 2023Towards Responsible Governance of Biological Design Tools27 Nov 2023
- 2023BadLlama: cheaply removing safety fine-tuning from Llama 2-Chat 13B31 Oct 2023
- 2023Managing extreme AI risks amid rapid progress26 Oct 2023
- 2023Will releasing the weights of future large language models grant widespread access to pandemic agents?25 Oct 2023
- 2023Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives29 Sep 2023
- 2023AI Deception: A Survey of Examples, Risks, and Potential Solutions28 Aug 2023
- 2023Frontier AI Regulation: Managing Emerging Risks to Public Safety6 Jul 2023
- 2023Hoodwinked: Deception and Cooperation in a Text-Based Game for Language Models5 Jul 2023
- 2023An Overview of Catastrophic AI Risks21 Jun 2023
- 2024Large Language Model Safety: A Holistic Surveycited by 59
- 2024A Safe Harbor for AI Evaluation and Red Teamingcited by 47
- 2024Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Researchcited by 15
- 2024CodeUnlearn: Amortized Zero-Shot Machine Unlearning in Language Models Using Discrete Conceptcited by 1
- 2026An Early Warning of Emerging Biosecurity Risks in Frontier LLMscited by 1
- 2026Sockpuppetting: Jailbreaking LLMs by Combining Prefilling with Optimizationcited by 0
- 2026An Early Warning of Emerging Biosecurity Risks in Frontier LLMs20 Jul 2026
- 2026Sockpuppetting: Jailbreaking LLMs by Combining Prefilling with Optimization19 Jan 2026
- 2024Large Language Model Safety: A Holistic Survey23 Dec 2024
- 2024Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research9 Dec 2024
- 2024CodeUnlearn: Amortized Zero-Shot Machine Unlearning in Language Models Using Discrete Concept8 Oct 2024
- 2024A Safe Harbor for AI Evaluation and Red Teaming7 Mar 2024
- 2025A Survey on Training-free Alignment of Large Language Models12 Aug 2025
- 2023An Overview of Catastrophic AI Riskscited by 222
- 2023AI Deception: A Survey of Examples, Risks, and Potential Solutionscited by 188
- 2024AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathwayscited by 127
- 2024Black-Box Access is Insufficient for Rigorous AI Auditscited by 126
- 2023Frontier AI Regulation: Managing Emerging Risks to Public Safetycited by 98
- 2024Visibility into AI Agentscited by 79
- 2023Managing extreme AI risks amid rapid progresscited by 48
- 2023Hoodwinked: Deception and Cooperation in a Text-Based Game for Language Modelscited by 47
- 2023BadLlama: cheaply removing safety fine-tuning from Llama 2-Chat 13Bcited by 36
- 2024Adversaries Can Misuse Combinations of Safe Modelscited by 32
- 2023Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectivescited by 31
- 2023Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Modelscited by 7
12 most cited of 25 All 25 (JSON)
- 2026Artificial intelligence and biosecurity: capabilities, threat pathways, and defense-in-depth governance14 Sep 2026
- 2026FUSE: An Evaluating Framework for Dangerous Capabilities of LLMs2 Sep 2026
- 2026A Blind Spot in Alignment: Quantifying Biosecurity Risks in Large Language Models3 Aug 2026
- 2026A Threshold Exceedance Framework for CBRN Uplift Evaluation in Frontier Language Models13 Jul 2026
- 2026RefusalBench: Why Refusal Rate Misranks Frontier LLMs on Biological Research Prompts20 May 2026
- 2026Measuring Mid-2025 LLM-Assistance on Novice Performance in Biology18 Feb 2026
- 2025Misalignment or misuse? The AGI alignment tradeoff4 Jun 2025
- 2025Can Large Language Models Design Biological Weapons? Evaluating Moremi Bio22 May 2025
- 2025Mapping AI Benchmark Data to Quantitative Risk Estimates Through Expert Elicitation6 Mar 2025
- 2025Supervision policies can shape long-term risk management in general-purpose AI models10 Jan 2025
- 2024Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems30 Oct 2024
- 2024To Err is AI : A Case Study Informing LLM Flaw Reporting Practices15 Oct 2024
12 newest of 25 All 25 (JSON)